WO2006092892A1

WO2006092892A1 - Method of evaluating fertilizer qualities, appratus for evaluating fertilizer qualities and program for evaluating fertilizer qualities

Info

Publication number: WO2006092892A1
Application number: PCT/JP2005/021580
Authority: WO
Inventors: Eiichi Tamiya; Miyuki Chikae
Original assignee: Japan Advanced Institute Of Science And Technology
Priority date: 2005-03-02
Filing date: 2005-11-24
Publication date: 2006-09-08
Also published as: JP4019153B2; JP2006242723A

Abstract

In a site of, for example, composting organic wastes, the germination index of a fertilizer can be accurately estimated and the qualities of the fertilizer such as the extent of maturation are quickly, conveniently and highly accurately evaluated. At least two factors selected from the physicochemical indexes and enzymatic activities of an extract of a fertilizer to be evaluated are obtained. Then, these factors are assigned to definite numerical formula to estimate the germination index of the fertilizer. The definite numerical formula as described above is a multiple regression formula obtained by a multiple-regression analysis on the germination indexes and the above-described factors of fertilizers similar to the fertilizer to be evaluated. The physicochemical index(es) as described above mean at least one factor selected from the group consisting of pH, ammonium ion concentration, nitrate ion concentration, electrical conductivity, temperature and redox potential. The enzymatic activities as described above are enzymatic activities indicating microbial activities.

Description

Specification

Fertilizer quality evaluation method, fertilizer quality evaluation device, and fertilizer quality evaluation program

The present invention relates to a fertilizer quality evaluation method, a fertilizer quality evaluation apparatus, and a fertilizer quality evaluation program for evaluating fertilizer quality such as the degree of maturity of compost.

Background art

[0002] Conventionally, incineration treatment and landfill treatment have been generally carried out as waste treatment. However, since environmental problems have been actively discussed in recent years, recycling of waste to new materials, energy, etc. Reuse is progressing. On the other hand, the amount of kitchen waste and food processing factory residues is increasing, and how to deal with organic waste, a by-product of city life, is a major issue. ing.

[0003] Under such circumstances, composting of organic waste has attracted attention. Composting organic waste is an effective way to prevent energy loss. In addition, compost made from kitchen waste has been reported to be less contaminated with heavy metals and other toxic substances with high nitrogen, phosphorus and potassium contents. In this way, organic waste can become high-quality compost, so it is expected to build a composting system for organic waste.

In order to further develop and expand the 1S recycling plant, it is essential to stabilize the quality of the compost supplied and to ensure that the composting system is suitable for cost.

[0004] Meanwhile, the quality of fertilizers such as compost is determined by the content of active ingredients, maturity, toxicity, etc., and quality evaluation is important in considering the compost process and the application of the product fertilizer. For example, when immature compost is applied, the crop is severely nitrogen starved and oxygen is consumed near the crop roots to become deficient as it is consumed to decompose the compost. Furthermore, the application of immature compost also causes the disadvantage of inhibiting growth of plants by producing growth inhibitory substances such as ammonia, ethylene oxide, and various organic acids. As described above, in order to prevent the shipment of immature compost, it is strongly established to establish a technology to evaluate the degree of maturity of compost and whether it is sufficiently mature and to evaluate the quality of compost quickly and easily. It has been demanded.

[0005] Therefore, various methods for evaluating maturity have been proposed so far! For example, on site Appropriate maturity evaluation methods have been proposed, such as an external scoring method, product temperature (sediment temperature) evaluation method, color tone evaluation method, odor evaluation method, touch evaluation method, plastic bag evaluation method, and earthworm evaluation method. (For example, refer nonpatent literature 1 etc.). In addition, methods for evaluating maturity using chemical analysis include CZN ratio, nitric acid detection method, BOD, COD evaluation method, reducing sugar ratio evaluation method, humus color difference evaluation method, cation exchange (CEC) method, Examples include a circular filter paper chromatography method and a gel mouth matography method (for example, see Non-Patent Document 1). However, each of the evaluation methods has problems such as limited applicable compost and complicated evaluation work, and is still insufficient in terms of accuracy of maturity evaluation. It is.

[0006] As a method for evaluating maturity, a method using the response of a plant has also been proposed. The evaluation method using the response of the plant is very effective for the examination of compost maturity and its evaluation as an organic fertilizer. Germination index (hereinafter referred to as GI) method is one of the methods for assessing maturity using plant responses, and is the most sensitive parameter for assessing compost toxicity and maturity. Known as one of the GI is determined by a germination test.Specifically, a compost water extract immediately after extraction is added to a plastic petri dish with filter paper, and seeds of plants such as komatsuna are sown on the filter paper, and in a dark place at 26 ° C. After 48 hours of incubation, the number of germinated seeds and root length are measured, and the ratio of the number of germinated seeds and root length in the control plot (distilled water) is calculated. In order to increase the accuracy of the GI, it is necessary to perform the germination test described above for a large number of samples. A petri dish containing filter paper is used as an instrument for measuring force, and it takes time to grow plant seeds. There is a problem in that it takes time and labor to maintain the growth environment for growing seeds and to measure after the growth.

[0007] Therefore, a visibility cultivation tank having an open top, a landing member for germinating seeds of a plant accommodated in the cultivation tank, and a germination from the landing member while being accommodated in the cultivation tank. And a supporting substrate provided in parallel with a cylindrical growth holder having visibility for growing plants, and a flooring member is provided at a lower portion of the growth holder and is provided in a visibility cultivation tank. Has proposed a plant growth measuring instrument provided with a scale for measuring the degree of growth of a plant growing in a growth holder (see, for example, Patent Document 1). According to the invention described in Patent Document 1, a germination test can be performed with a simple operation even when the number of samples is large. It is said that the maturity of organic matter such as compost can be judged.

Non-Patent Document 1: Shinichiro Kanazawa, Use of Organic Resources in Agricultural Lands 1st Report Compost Maturity Test, Regeneration and Utilization, Vol.27 No.102 (2004)

Patent Document 1: JP 2004-201586

Disclosure of the invention

Problems to be solved by the invention

However, in the invention of Patent Document 1 as well, the GI is obtained by actually carrying out a germination test, so that it takes a long time to measure and the measurement is complicated. Therefore, it is not realistic considering the actual use of composting that requires quick and accurate judgment. Therefore, there is a strong need for a technology that estimates GI with high accuracy without conducting germination tests and evaluates the maturity of fertilizers such as compost, but such a technology has not yet been established. is there.

[0009] Therefore, the present invention has been proposed in view of such conventional circumstances, and it is possible to estimate the germination index of fertilizer with high accuracy, for example, at the site of composting organic waste.

An object of the present invention is to provide a fertilizer quality evaluation method, a quality evaluation apparatus, and a fertilizer quality evaluation program capable of quickly, simply and accurately evaluating the quality of fertilizer such as maturity.

Means for solving the problem

[0010] The inventors of the present invention have made studies over a long period of time in order to achieve the above-described object. As a result, we obtain knowledge that there is a good correlation between the germination index as an index to evaluate the maturity of fertilizer and the specific physico-chemical index and specific enzyme activity of fertilizer water extract It reached to. The present invention has been completed based on such findings.

[0011] That is, the fertilizer quality evaluation method according to the present invention acquires at least two types of information selected from the physicochemical index and enzyme activity of the fertilizer extract to be evaluated, and stores the information as a predetermined value. The germination index of the fertilizer is estimated by applying to a calculation formula

[0012] In addition, the fertilizer quality evaluation apparatus according to the present invention is an information collection device that acquires at least two types of information selected from the physicochemical index and enzyme activity of the extract of the fertilizer to be evaluated. And a control unit that estimates the germination index of the fertilizer by applying the information to a predetermined calculation formula.

[0013] In the present invention, as a parameter for determining a germination index that is an index of maturity, a physicochemical index such as pH and ammonium ion concentration of an extract of fertilizer, an enzyme that represents the activity of microorganisms By combining a plurality of parameters selected from enzyme activity such as activity, the germination index of the fertilizer can be estimated with high accuracy. Since the germination index expresses the maturity of the fertilizer with high sensitivity, the maturity of the fertilizer can be accurately grasped by the present invention. Measurement of physicochemical indicators such as pH measurement, ammonia ion concentration measurement, and enzyme activity measurement take less time for measurement compared to germination tests, etc. The assessment of maturity is done quickly and easily.

[0014] Furthermore, the fertilizer quality evaluation program according to the present invention acquires information for acquiring at least two types of information selected from the physical and physiological indices and enzyme activity of the fertilizer extract to be evaluated. An input function and a control function for estimating the germination index of the fertilizer by applying the information to a predetermined calculation formula are realized in a computer.

[0015] According to the fertilizer quality evaluation program configured as described above, in order to make the computer estimate the germination index of the fertilizer, the computer can quickly and easily determine the germination index as an index of the fertilizer maturity. It is possible to estimate with high accuracy and evaluate the quality of fertilizer. The invention's effect

[0016] According to the present invention, the germination index of the fertilizer is estimated with high accuracy by measuring the physical and physiological indices such as the pH and ammonium ion concentration of the fertilizer extract, the enzyme activity, etc. Since the degree of maturity can be evaluated, it is possible to provide a fertilizer quality evaluation method capable of evaluating fertilizer quality quickly, easily and with high accuracy.

[0017] Further, according to the fertilizer quality evaluation apparatus of the present invention, it is possible to estimate the germination index, which is an index of the fertilizer maturity, with high accuracy, and it is possible to evaluate the fertilizer quality quickly, easily and with high accuracy. Is possible. In addition, by using the fertilizer quality evaluation apparatus of the present invention, a high-quality fertilizer can be stably supplied in a composting system that composts city waste and the like.

[0018] Furthermore, according to the fertilizer quality evaluation program of the present invention, since the fertilizer quality evaluation can be executed by a computer, the fertilizer quality can be evaluated quickly, easily and with high accuracy. Can do.

Brief Description of Drawings

FIG. 1 is a schematic diagram showing an example of a fertilizer quality evaluation apparatus of the present invention.

FIG. 2 is a characteristic diagram showing the GI change over time of fertilizer produced by the deposition method.

FIG. 3 is a characteristic diagram showing the GI change with time of compost produced by the reactor method.

FIG. 4 is a characteristic diagram showing the relationship between actual GI and estimated GI in deposition method A.

FIG. 5 is a characteristic diagram showing the relationship between actual GI and estimated GI in deposition method B.

FIG. 6 is a characteristic diagram showing the relationship between actual GI and estimated GI in reactor method A.

FIG. 7 is a characteristic diagram showing the relationship between actual GI and estimated GI in reactor method B.

Explanation of symbols

[0020] 1 fertilizer quality evaluation device, 2 information acquisition input unit, 3 control unit, 4 storage unit, 5 display unit BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, a fertilizer quality evaluation method, a fertilizer quality evaluation device, and a fertilizer quality evaluation program according to the present invention will be described in detail.

In the fertilizer quality evaluation method of the present invention, the germination index of the fertilizer to be evaluated is estimated, the maturity of the fertilizer is evaluated using the estimated germination index as an index, and the quality of the fertilizer is evaluated.

[0023] Here, the fertilizer refers to compost produced by fermenting or rotting organic waste containing organic matter, organic fertilizer produced by drying raw materials such as fish meal, soil improvement material, etc. The present invention is applicable to all of these fertilizers. The present invention is also applicable to fertilizers that are in the process of production, such as immature compost. As raw materials for compost and fertilizer, raw household wastes and facilities, food waste, food residues from food processing factories, pruned branches of wood, rice chaff, cows, pigs, chickens, etc. It is not particularly limited, such as livestock manure and husk (bark compost raw material), and any conventionally known waste can be used.

[0024] Germination index (hereinafter referred to as GI) is a measure of fertilizer maturity based on the degree of germination and growth of plant seeds, and is an index of fertilizer maturity. . For example, GI measures the number of seeds and root length germinated by germination tests, and the number of seeds and root length germinated in the control plot (distilled water), and applies each data to the following formula (1). Is required. In germination tests, for example, immediately after extraction on a petri dish with filter paper. Add 10 ml of fertilizer water extract or water (distilled water), sow seeds of plants such as Komatsuna on filter paper, incubate in dark place at 26 ° C for 48 hours, and then determine the number of germinated seeds and the length of roots. taking measurement. In calculating GI, in addition to the root length, stem length or the like may be used.

[0025] [Equation 1]

_GI value Germination rate of measurement sample X average root length X 100.

Germination rate of control plot X Average root length of control plot

[0026] In the present invention, the GI of the fertilizer to be evaluated is estimated in order to evaluate the maturity of the fertilizer. Prior to the estimation of the GI, an expression necessary for the estimation of the GI is obtained in advance as described below. Wait

<o

[0027] First, a fertilizer similar to the fertilizer to be evaluated is prepared separately, an appropriate number is sampled at an appropriate time interval with this similar fertilizer force, and an extract is prepared by, for example, water extraction.

[0028] Here, the fertilizer similar to the fertilizer to be evaluated means a fertilizer manufactured using a raw material substantially the same as the raw material of the fertilizer to be evaluated. From the viewpoint of improving the estimation accuracy of GI, it is preferable to use a raw material having the same composition as the fertilizer to be evaluated. However, due to the nature of the raw material (waste), it is difficult to accurately reproduce the raw material composition. Therefore, the raw material substantially the same as the raw material of the fertilizer to be evaluated may be, for example, a raw material having the same source as the raw material of the fertilizer to be evaluated. Specifically, a raw material that is substantially the same as the raw material for the fertilizer to be evaluated is, for example, a match made with the species of livestock such as cow dung, pig dung, chicken dung, etc. It is. Furthermore, when the fertilizer raw material to be evaluated is raw garbage, it is preferable to match the raw material of the fertilizer to be evaluated with the presence or absence of fish meal.

[0029] From the viewpoint of further improving the estimation accuracy, it is preferable to use a fertilizer manufactured by a manufacturing method substantially similar to the fertilizer to be evaluated as a similar fertilizer. The fertilizer production method is preferably the same as the fertilizer to be evaluated, but may be slightly different.

[0030] Next, a normal germination test is performed using the prepared extract, and, for example, GI is obtained based on the formula (1).

[0031] Here, as the physicochemical index of the fertilizer extract, various kinds of physicochemical indices of conventionally known aqueous solutions can be mentioned, for example, pH, ammonium ion concentration, glass Examples include acid ion concentration, electrical conductivity, temperature, oxidation-reduction potential, etc. Among them, it is desirable to employ pH and ammonium ion concentration as physical physical indicators.

[0032] As the enzyme activity, it is preferable to employ the enzyme activity of an enzyme selected from the group of enzymes representing the activity of microorganisms capable of targeting the activity of any conventionally known enzyme. Examples of the enzyme activity representing the activity of microorganisms include protease activity, amylase activity, cellulase activity, alkaline phosphatase activity, acid phosphatase activity, phosphohydrase activity, esterase activity, esterase lipase activity, lipase activity, leucine arylamidase activity. , Noryalylamidase activity, Cystine arylamidase activity, Trypsin activity, Chymotrypsin activity, a-galatatosidase activity, β-galactosidase activity, β-dalclonidase activity, a-darcosidase activity, β-darcosidase activity, Ν acetyl-β-darcosamini Examples include dase activity, at mannosidase activity, and a fucosidase activity. As the enzyme activity, an optimum one may be selected from the above according to the type of fertilizer, and it is preferable to select acid phosphatase activity and esterase activity, especially when evaluating the quality of compost. Good.

[0033] Then, the relationship between the acquired GI and information such as a physicochemical index and enzyme activity is analyzed. Specifically, by performing multiple regression analysis on the relationship between the acquired GI and the corresponding information such as physicochemical indicators and enzyme activities, GI is the target variable, and the physicochemical indicators and enzyme activity are explanatory variables. The multiple regression equation is obtained. It is preferable to select a large number of parameters from among the physicochemical indicators listed above and the enzyme activities listed above as parameters for the analysis, but at least two of these parameters are preferable. Should be selected.

In the present invention, using the multiple regression equation, the GI of the fertilizer to be evaluated is estimated and the maturity is evaluated as follows.

[0035] First, the fertilizer to be evaluated is extracted using, for example, water to prepare an extract.

[0036] Next, the prepared extract is subjected to at least two kinds selected from measurement of physicochemical index and measurement of enzyme activity. Get at least two types of information to be selected. That is, information on parameters adopted in the multiple regression equation is acquired.

[0037] Next, by applying at least two types of information selected from the physicochemical index and enzyme activity of the extract of the fertilizer to be evaluated to the multiple regression equation obtained in advance, the fertilizer to be evaluated Estimate GI. The obtained estimated value of GI shows a good correlation with the measured value of GI, so the fertilizer maturity is evaluated using this estimated GI as an index. For example, GI 50% is the standard for determining whether or not the fertilizer is fully ripened. The standard value of GI should be determined appropriately according to the type of fertilizer!

[0038] As described above, these relationships are analyzed in advance using the physico-chemical index, enzyme activity, etc. of the fertilizer extract, and the obtained formula is used to determine the physicochemical index, enzyme activity, etc. of the fertilizer to be evaluated. By applying the corresponding information, it is possible to estimate GI with high accuracy. In particular, by selecting the pH, ammonia ion concentration, and enzyme activity as parameters, GI can be estimated accurately and easily. In addition to the pH, ammonia ion concentration, enzyme activity, and other suitable physicochemical indicators, enzyme activity, etc. as described above for fertilizer extracts, multiple parameters can be used as parameters for multiple regression analysis. When selecting, the accuracy of GI estimation can be further improved.

[0039] By the fertilizer quality evaluation method of the present invention as described above, the maturity of the fertilizer to be evaluated can be evaluated with high accuracy, and the quality of the fertilizer can be evaluated with high accuracy. In the present invention, the measurement time for physicochemical indicators and enzyme activities is short, and GI can be estimated based on information that can be obtained by simple operations, and there is no need to perform a germination test for each evaluation. In other words, the labor and time required for measurement are drastically reduced, and the fertilizer maturity can be evaluated quickly and easily. Therefore, according to the present invention, it is possible to evaluate the quality of fertilizers quickly, simply, and with high accuracy at, for example, a composting site, and contribute to the supply of high-quality fertilizers. In addition, the present invention can be applied to quality evaluation of various types of fertilizers regardless of raw materials and production methods by appropriately changing the coefficient of the multiple regression equation.

[0040] Next, the fertilizer quality evaluation program of the present invention will be described. The fertilizer quality evaluation program of the present invention is a program for controlling the operation of a computer and performing the quality evaluation of the fertilizer as described above. The pH and ammonia for the fertilizer extract to be evaluated are as follows. Physicochemical indicators such as the concentration of niobium ions, enzyme activity such as enzyme activity representing the activity of microorganisms Information acquisition input function that acquires at least two types of information selected, and the acquired information is applied to a predetermined calculation formula This is a program that allows a computer to realize a control function for estimating the germination index of fertilizer. With a powerful program, the fertilizer germination index can be automatically estimated by a computer, and the quality of the fertilizer can be grasped quickly, easily and with high accuracy.

[0041] Next, the fertilizer quality evaluation apparatus of the present invention will be described. An example of the fertilizer quality evaluation device of the present invention is shown in FIG. The fertilizer quality evaluation device 1 includes an information acquisition / input unit 2 for obtaining various information such as compost, physicochemical indicators and fertilizer extracts, enzyme activity, etc., a control unit 3, a program executed by the control unit 3, It basically includes a storage unit 4 in which various data such as multiple regression equations necessary for GI value estimation are stored. The fertilizer quality evaluation device 1 includes a display and the like, and includes a display unit 5 that displays information acquired by the information acquisition input unit 2 and results calculated by the control unit 3.

[0042] In the following, the information acquisition input unit 2 will be described by taking as an example a configuration that can acquire information on pH, ammonia ion concentration, acid phosphatase activity, and esterase activity. The invention is not limited to these, and it is needless to say that it is possible to obtain information on at least two of the physicochemical indicators and enzyme activities as described above! /.

[0043] The information acquisition input unit 2 includes, for example, a pH measurement unit 2a that performs pH measurement on the fertilizer extract, an ammonium ion concentration measurement unit 2b that performs measurement of the ammonium ion concentration, and a second unit that performs measurement of acid phosphatase activity. The first enzyme activity measurement unit 2c and the second enzyme activity measurement unit 2d that measure esterase activity are also configured to obtain information on the pH, ammonia concentration, enzyme activity, etc. of the fertilizer extract. The pH measuring unit 2a, the ammonium ion concentration measuring unit 2b, the first enzyme activity measuring unit 2c, and the second enzyme activity measuring unit 2d are respectively a conventionally known pH sensor, ammonium ion concentration sensor, and enzyme. It may be composed of an activity sensor or the like, and may also be composed of a test paper force such as a pH test paper. The configuration of the information acquisition / input unit 2 is not limited to this as long as various types of information can be acquired. For example, each measurement unit may be externally attached to the fertilizer quality evaluation apparatus 1. Also information The acquisition unit 2 may be configured by an input device such as a keyboard. In this case, information on pH, ammonium ion concentration, acid phosphatase activity, esterase activity, etc. is measured separately using a test paper such as a pH test paper, and input via the input device of the information acquisition unit 2. Is done.

The control unit 3 and the storage unit 4 are configured by a computer device including a CPU and a storage device such as a ROM, a RAM, and a magnetic disk, for example, and control each unit of the fertilizer quality evaluation device 1. The control unit 3 stores in advance in the storage unit 4 and reads out a program, a predetermined calculation formula, etc. as necessary, and at the same time the pH of the fertilizer extract obtained by the information acquisition input unit 2, the ammonia ion concentration, Apply information about enzyme activity, etc., and calculate. The calculation formula is, for example, a measurement variable GI of an extract of a fertilizer similar to the target fertilizer to be evaluated as a target variable, and at least two kinds selected from pH, ammonia ion concentration, and enzyme activity column as explanatory variables. It is a regression equation.

A method for estimating the GI of fertilizer using the fertilizer quality evaluation apparatus 1 having such a configuration and evaluating the maturity will be described. In the following, the information acquisition unit 2 is composed of a pH sensor, an ammonia ion concentration sensor, an enzyme activity sensor, and performs both measurement and information acquisition of the pH, ammonium ion concentration and enzyme activity of the fertilizer extract. Take as an example.

[0046] First, the information acquisition input unit 2 is immersed, for example, in the fertilizer extract to be evaluated, and various measurements are performed in the information acquisition input unit 2 to determine pH, ammonia ion concentration, acid phosphatase activity, esterase activity. Get at least two types of information to be selected. The acquired information is converted into an electric signal or the like and sent to the control unit 3 or the storage unit 4.

Next, the control unit 3 reads, for example, a multiple regression equation stored in the storage unit 4 in advance, and calculates the estimated GI of the fertilizer to be evaluated by applying the information and performing calculation. The estimated GI calculated by the control unit 3 and the maturity evaluation based on the estimated GI are displayed, for example, on the display unit 5 or the like.

[0048] As described above, according to the fertilizer quality evaluation apparatus 1, the GI can be estimated without performing a germination test, and the quality such as the maturity of the fertilizer can be evaluated quickly and accurately with a simple operation. . In addition, the fertilizer quality evaluation device 1 can be used as a growing device used for germination tests when obtaining GI. There is an advantage that it is small in size. Furthermore, since the fertilizer quality evaluation apparatus 1 is easier to maintain than the growing equipment used in the germination test, it is possible to evaluate the quality of the fertilizer at a low cost.

Example

[0049] Examples of the present invention will be described below based on experimental results.

[0050] <Composting and sampling>

In this experiment, compost was made using the pruned branches of park trees and roadside trees and school meals left from Kaga Pass in Ishikawa Prefecture as raw materials. School lunches were collected from junior high schools in the city. In addition to the pruned branches and rice husks made into chips in these school meal residues so that the water content is about 65%, commercially available fermentation-promoting microorganisms (A: uron C or B: Uchijou) are mixed, The mixture was mechanically stirred at 65 ° C for 3 hours. The mixture was then processed in two ways. One is piled up in the yard and stirred twice a week (deposition method). The other is a method using a reactor manufactured by Mizushima Bussan Co., Ltd. (reactor method). In the reactor, oxygen is supplied naturally to the mesh part force on the side. Each week, three force samplings within the same treatment group were performed. At that time, the product temperature was also measured. The composting test was conducted in Sakai in October and the following March. The average daily temperature in the city during the test period ranged from 1.6 ° C to 11.7 ° C. Table 1 shows the various production methods and their raw material components.

[0051] [Table 1] Gross weight Raw material components (%)

Compost name Manufacturing method

(kg) Pruned branch Chimigari rice 'bread Vegetable scrap Waste rice Soil A Sedimentation / stirring 6615 13 13 19 26 29 Sedimentation B Sedimentation / stirring 4540 13 13 20 25 29 Reactor A Static ventilation system 400 13 13 19 25 30 Reactor B Static ventilation system 400 14 14 18 37 28

[0052] <Physicochemical analysis>

The moisture content was also calculated by the amount of decrease after compost was heated at 105 ° C for 5 hours. Total carbon and total nitrogen are measured with an automatic elemental analyzer (trade name Vario EL ΙΠ), and the CZN ratio is From the above.

[0053] The compost water extract was prepared by filtering the compost with 10 times the amount of distilled water per dry weight, stirring at 120 rpm for 30 minutes, and then filtering. The pH and electrical conductivity of the extract were immediately measured. The extract was stored at -20 ° C. The ammonium ion concentration and nitrate ion concentration of the extract were measured by a colorimetric method.

[0054] <Enzyme activity: Agar plate assembly>

Protease, amylase and cellulase activities in the water extract were detected according to the following method.

(1) Protease: 2 g agar, 0.1 lg sodium azide is dissolved in 0.1 M phosphate buffer (PH 7.0) by heating, and then ImL 1M calcium chloride, lOmL 1% casein added. 15 mL of these mixtures were cooled in a 9 cm diameter plastic petri dish.

(2) Amylase: 0.8 g soluble starch and 2 g agar were dissolved in lOOmL 1.OM acetate buffer (pH 5.0). It was put into a petri dish and cooled as described above.

(3) Cellulase: 0.8 g of carboxymethyl cellulose and 2 g of agar were dissolved in lOOmL of 1.0 M acetate buffer (pH 5.0). As above, it was cooled in a petri dish.

[0055] A hole with a diameter of 3 mm was formed in each agar plate of (1) to (3) with a cork borer, and 10 / z L of a compost water extract was dropped into the hole and incubated at 55 ° C for 18 hours. After incubation, protease activity could be confirmed by observing a clear halo around the sample hole. Amylase activity was visualized by adding 0.2% potassium iodide and 0.02% iodine solution, and cellulase activity by adding 0.1% Congo red solution followed by washing to visualize the halo around the sample hole. The diameter of each mouth was measured with a grip.

[0056] <Enzyme activity: Apizzaim Atssey>

Nineteen enzyme activities of the compost water extract were measured using the brand name Apizza, which is an Atsey kit. The measured enzymes were alkaline phosphatase, acid phosphatase, phosphohydrase, esterase, esterase monolipase, lipase, leucine amylamidase, nornarlylamidase, cystine arylamidase, trypsin, chymo Trypsin, a galactosidase, β-galactosidase, β-glucuronidase, α-darcosidase, / 3-darcosidase, N-acetylyl β-darcosaminidase, a mannosidase and α-fucosidase.

Enzyme activity was achieved by adding 60 μL of reagent to a well coated with a specific chromogenic substrate for each enzyme, incubating at 36 ° C for 4 hours, and then stopping the reaction with reagents A and B to cause color development. . The enzyme activity was judged by visual comparison of the color density with the attached color chart.

[0057] <Komatsuna germination test>

In a plastic petri dish with filter paper, 10 mL of the water extract immediately after extraction was placed, 30 seeds of Komatsuna seeds were seeded, and incubated in a dark place at 26 ° C for 48 hours. Thereafter, the number of germinated seeds and the length of the roots were measured. As a control, the same operation was performed using 10 mL of distilled water. GI was calculated according to the above equation (1) proposed by Zucconi et al. (1981). About 50% of GI is a measure of the presence or absence of inhibition of the compost growth. Since it is known that the storage condition of the sample affects the parameter value of the maturity assessment, in this experiment, a compost extract was prepared immediately after sampling, and the extract immediately after extraction was used for the germination test. It was. Figure 2 shows the GI change in the deposition method, and Figure 3 shows the GI change in the reactor method.

[0058] <Statistical analysis>

The relationship between GI and other parameters (moisture content, temperature, pH, electrical conductivity, ammonia ion concentration, nitrate ion concentration, carbon and nitrogen content, CZN ratio, 22 enzyme activities) A linear multiple regression analysis was performed by the stepwise method using the famous statview. The correlation between each parameter was also analyzed using the same software.

[0059] GI (measured value) obtained by Komatsuna germination test and corresponding water content, temperature, pH, electrical conductivity, ammonium ion concentration, nitrate ion concentration, carbon content, nitrogen content, CZN The relationship between the ratio and the activity of the 22 enzymes was analyzed using stepwise linear multiple regression analysis. The number of samples was 159 samples for all composting systems. Table 2 shows the parameters selected in the multiple regression analysis and their characteristics. The coefficient of determination R ² in the multiple regression model is 0.896, I that these parameters force estimated GI is that good agreement with the measured values ChikaraTsuta.

[0060] [Table 2] Factor Regression coefficient Standard error S.R.C. * F-value Intercept -43.028 13.312 -43.028 10.447 pH 15.430 1.341 0.703 132.478 Electricity 1 4.982 2.019 0.132 6.088

NH ₄ + concentration -0.291 0.039 -0.489 55.463

N0 ₃ -concentration -0.183 0.052 -0.133 12.203 Temperature -0.244 0.094 -0.140 6.725 Alkaline phosphatase -Se '3.181 1.178-0.183 7.284 Acid phosphatase -5.504 1.026 -0.307 28.761 Phosphohydroxylase -2.962 1.429 -0.110 4.297 Esterase- 9.675 1.954 -0.267 24.526 β-glucosidase 3.530 1.168 0.150 9.133

S.R.C. *: Standard regression coefficient

[0061] To make the model even simpler, we selected four parameters with high F values from Table 2, that is, ρΗ, ammonium ion concentration, acid phosphatase activity, and esterase activity. went. As a result, the following formula (2) was obtained. In the formula (2), “Ν Η +” represents ammonium ion concentration (mgZD. “AP” represents acid phosphatase.

Four

Activity, “E” represents esterase activity.

[0062] GI = — 60. 239 + 19. 057pH— 0. 238 [NH +] — 5. 381AP— 5. 117E

Four

Formula (2)

[0063] The coefficient of determination R ² in the multiple regression model was 0.791. Figures 4 to 7 show the correlation between the GI, which is estimated for these parameter forces, and the actual GI in each composting system. Pearson's correlation coefficients (r) for deposition method A, deposition method B, reactor method A, and reactor method B were 0.96, 0.93, 0.84, and 0.89, respectively. The slope of the regression equation was about 1 in the sedimentation method. In the force reactor method, both A and B had a smaller force than the deposition method. When estimating GI, it may be more effective to perform analysis for each composting method.

[0064] The four parameters selected this time (pH, ammonia ion concentration, acid phosphate) Tase activity and esterase activity) are not substantially correlated with GI by themselves (ri, 0.369, 0.019, 0.047, 0.321, respectively). For this reason, the GI cannot be estimated by the power of these norms themselves. However, GI can be estimated by combining these, and multiple regression analysis was effective in selecting parameters.

As is clear from Figs. 4 to 7, the estimated GI showed a good correlation with the actual GI. Therefore, from the above experiment, multiple regression analysis was performed on GI and the corresponding physico-physical indices and enzyme activity, and a multiple regression equation such as Equation (2) was obtained. By applying the fertilizer data to be evaluated to the regression equation, it is clear that the GI of the fertilizer can be estimated with high accuracy. In the fertilizer used in this experiment, the pH, ammonia ion concentration, acid phosphatase activity, and esterase activity were selected as the parameters, and a high-precision estimation of GI was realized with a simple model.

Claims

The scope of the claims

[1] Obtain at least two types of information selected from the physicochemical index and enzyme activity of the fertilizer extract to be evaluated, and apply the information to a predetermined calculation formula to estimate the germination index of the fertilizer Fertilizer quality evaluation method characterized by that.

[2] The predetermined calculation formula is obtained by performing a multiple regression analysis on a germination index for a fertilizer to be evaluated and a similar fertilizer, and at least two types of information selected for a physicochemical index and enzyme activity. The fertilizer quality evaluation method according to claim 1, wherein the fertilizer quality evaluation method is a multiple regression equation.

[3] The physicochemical index is at least one selected from the group force consisting of pH, ammonia ion concentration, nitrate ion concentration, electrical conductivity, temperature, and acid reduction potential force. The fertilizer quality evaluation method according to claim 1 or 2.

4. The method for evaluating fertilizer quality according to claim 3, wherein the physicochemical index is pH and ammonium ion concentration.

[5] The method for evaluating fertilizer quality according to any one of claims 1 to 4, wherein the enzyme activity is an enzyme activity representing the activity of a microorganism.

[6] The enzyme activity representing the activity of the microorganism is protease activity, amylase activity, cellulase activity, alkaline phosphatase activity, acid phosphatase activity, phosphohydrase activity, esterase activity, esterase lipase activity. Lipase activity, oral isinalarylamidase activity, norarylamidase activity, cystine arylamidase activity, trypsin activity, chymotrypsin activity, a-galatatosidase activity, β-galatatosidase activity, 13 dalcronidase activity, (X darcosidase 6. The activity according to claim 5, wherein the activity is at least one selected from the group consisting of 13 activity, 13 darcosidase activity, ァ -acetyleno β β darcosaminidase activity, a mannosidase activity and α-fucosidase activity. Fertilizer quality evaluation method.

[7] An information acquisition unit that acquires at least two types of information selected from the physicochemical index and the enzyme activity of the fertilizer extract to be evaluated;

A fertilizer quality evaluation apparatus, comprising: a controller that applies the information to a predetermined calculation formula to estimate a germination index of the fertilizer.

[8] The predetermined calculation formula is obtained by performing a multiple regression analysis on a germination index for a fertilizer similar to a fertilizer to be evaluated, a physicochemical index, and at least two types of information selected for enzyme activity. The fertilizer quality evaluation device according to claim 7, wherein the fertilizer quality evaluation device is a multiple regression equation.

[9] The physicochemical index is at least one selected from the group force consisting of pH, ammonia ion concentration, nitrate ion concentration, electrical conductivity, temperature, and acid reduction potential force. The fertilizer quality evaluation apparatus according to claim 7 or 8.

10. The fertilizer quality evaluation device according to claim 9, wherein the physicochemical index is pH and ammonium ion concentration.

[11] The fertilizer quality evaluation device according to any one of [7] to [10], wherein the enzyme activity is an enzyme activity representing the activity of a microorganism.

[12] The enzyme activity representing the activity of the microorganism is protease activity, amylase activity, cellulase activity, alkaline phosphatase activity, acid phosphatase activity, phosphohydrase activity, esterase activity, esterase lipase activity. Lipase activity, oral isinalarylamidase activity, norarylamidase activity, cystine arylamidase activity, trypsin activity, chymotrypsin activity, a-galatatosidase activity, β-galatatosidase activity, 13 dalcronidase activity, (X darcosidase 12. The fertilizer quality evaluation apparatus according to claim 11, wherein the fertilizer quality evaluation apparatus is at least one selected from the group consisting of: activity, 13 darcosidase activity, Ν-acetylinole β darcosaminidase activity, a mannosidase activity and α-fucosidase activity.

[13] An information acquisition and input function for acquiring at least two types of information selected from the physicochemical index and enzyme activity of the fertilizer extract to be evaluated;

A fertilizer quality evaluation program for causing a computer to realize a control function for estimating the germination index of the fertilizer by applying the information to a predetermined calculation formula.

[14] The predetermined calculation formula is obtained by performing a multiple regression analysis on a germination index for a fertilizer to be evaluated and similar fertilizers, and at least two types of information selected for physicochemical indicators and enzyme activity. 14. The fertilizer quality evaluation program according to claim 13, wherein the program is a multiple regression equation.

[15] The physicochemical index is at least one selected from the group force consisting of pH, ammonium ion concentration, nitrate ion concentration, electric conductivity, temperature, and acid reduction potential force. The fertilizer quality evaluation program according to claim 13 or 14.

16. The fertilizer quality evaluation program according to claim 15, wherein the physicochemical index is pH and ammonium ion concentration.

17. The fertilizer quality evaluation program according to any one of claims 13 to 16, wherein the enzyme activity is an enzyme activity representing the activity of a microorganism.

[18] The enzyme activity representing the activity of the microorganism is protease activity, amylase activity, cellulase activity, alkaline phosphatase activity, acid phosphatase activity, phosphohydrase activity, esterase activity, esterase lipase activity. Lipase activity, oral isynarylamidase activity, norarylamidase activity, cystinearylamidase activity, trypsin activity, chymotrypsin activity, a-galatatosidase activity, β-galatatosidase activity, 13 dalcronidase activity, (X darcosidase 18. The activity according to claim 17, wherein the activity is at least one selected from the group consisting of: 13 activity, 13 darcosidase activity, ァ -acetylenore β darcosaminidase activity, a mannosidase activity and α-fucosidase activity. Fertilizer quality evaluation program .